Work

RCNNshift: Moving Object Tracking with Kernel and Training-Free Spiking Neural Network

Year: 2025

Type: article

Abstract: Moving object tracking is essential in computer vision applications such as autonomous navigation and surveillance. Traditional kernel-based methods like Meanshift and Camshift are computationally efficient but often falter with complex motions and occlusions. Deep learning approaches improve performance but require significant computational resources and extensive pretraining. In this paper, we present RCNNshift, a novel tracking algorithm that integrates Three-Dimensional Random-Coupled Neural Networks (3DRCNN) with the kernel-based window update process, eliminating the need for pretraining. RCNNshift constructs a robust feature space by leveraging spatiotemporal information from 3DRCNN, enabling effective tracking with reduced computational overhead. Additionally, it employs a one-dimensional feature space for the kernel-based window update step, which can improve efficiency compared to conventional multi-dimensional approaches. We evaluated RCNNshift on multiple datasets, where it consistently outperformed Meanshift and Camshift in accuracy and reliability, particularly in high-resolution and stable remote sensing environments. RCNNshift offers a promising balance between performance and computational efficiency, making it suitable for real-world applications where resources are limited.

Source: ACM Transactions on Multimedia Computing Communications and Applications

DOI: 10.1145/3773283

Authors: Haoran Liu, Peng Li, Mingzhe Liu, Yiran Chen, Rui Yao

Institutions: Chengdu University of Technology, Wenzhou University of Technology

Language: English

Topic: Video Surveillance and Tracking Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: Natural Science Foundation of Zhejiang Province

Award: 大体积箱装核废物场景自适应智能检测与多模融合成像方法研究